You don’t have to blog only about your side projects; you can also share tutorials or your experience with a bootcamp, conference, or online course.
Data scientists are a mix of mathematicians, trend-spotters, and computer scientists. The data scientist’s role is to decipher large volumes of data and carry out further analysis to find trends in the data and gain a deeper insight into what it all means.
Once we have started as data scientists, we need to build a portfolio. A portfolio is a data science project (or set of projects) that you can show to people to explain what kind of data science work you can do.
A strong portfolio has two main parts: GitHub repositories and a blog. A GitHub repo hosts the code for a project, and the blog shows off your communication skills and the non-code part of your data science work. Most people don’t want to read through thousands of lines of code; they want a quick explanation of what you did and why it’s important.
A data science project starts with two things: a dataset which is interesting and a question to ask about it. When thinking about what data you want to use, the most important thing is to find data which interests you.
Building a portfolio doesn’t need to be a huge time commitment. Perfect is definitely the enemy of the good here. Something is better than nothing and employers are first and foremost looking for evidence that you can code and communicate about data.